An efficient clustering algorithm

被引：2

作者：

Zhang, YF ^{[1
]}

Mao, JL ^{[1
]}

Xiong, ZY ^{[1
]}

机构：

[1] Chongqing Univ, Dept Comp Sci, Chongqing 400044, Peoples R China

来源：

2003 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-5, PROCEEDINGS | 2003年

关键词：

clustering; the K-means algorithm; squared-error criterion;

D O I：

10.1109/ICMLC.2003.1264483

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Clustering analysis plays an important role in scientific research and commercial application. K-means algorithm is a widely used partition method in clustering. As the dataset's scale increases rapidly, it is difficult to use K-meansand deal with massive data. An improved K-means algorithm is presented. It can avoid getting into locally optimal solution in some degree, and reduce the probability of dividing one big cluster into two or more ones owing to the adoption of squared-error criterion. The experiments demonstrate that the improved K-means is more stable and more accurate.

引用

页码：261 / 265

页数：5

共 50 条

[41] An energy efficient clustering routing algorithm for wireless sensor networks [J].

LI LIDONG ShusongWEN XiangmingInstitute of Continuing Education School Beijing University of Posts and Telecommunications Beijing China .

TheJournalofChinaUniversitiesofPostsandTelecommunications, 2006, (03) :71-75

[42] Energy-Efficient Distributed Clustering Algorithm Based on Coverage [J].

Xu Yi ;

Xu Yong-qiang .

PROCEEDINGS OF THE NINTH INTERNATIONAL SYMPOSIUM ON DISTRIBUTED COMPUTING AND APPLICATIONS TO BUSINESS, ENGINEERING AND SCIENCE (DCABES 2010), 2010, :32-35

[43] An energy-efficient distributed clustering algorithm for heterogeneous WSNs [J].

Nadeem Javaid ;

Muhammad Babar Rasheed ;

Muhammad Imran ;

Mohsen Guizani ;

Zahoor Ali Khan ;

Turki Ali Alghamdi ;

Manzoor Ilahi .

EURASIP Journal on Wireless Communications and Networking, 2015

[44] Efficient Clustering Algorithm of Mining Consumers' Most Adaptive Products [J].

Jiang Yuyan ;

Chen Quan ;

Xing Gouzheng ;

Li Jie ;

Pi Xiaoyan .

2009 WRI WORLD CONGRESS ON SOFTWARE ENGINEERING, VOL 2, PROCEEDINGS, 2009, :301-305

[45] An efficient similarity searching algorithm based on clustering for time series [J].

Feng, Yucai ;

Jiang, Tao ;

Zhou, Yingbiao ;

Li, Junkui .

ADVANCES IN DATA MINING, PROCEEDINGS: MEDICAL APPLICATIONS, E-COMMERCE, MARKETING, AND THEORETICAL ASPECTS, 2008, 5077 :360-373

[46] An efficient clustering algorithm for mixed type attributes in large dataset [J].

Yin, R ;

Tan, ZF ;

Ren, JT ;

Chen, YQ .

PROCEEDINGS OF 2005 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-9, 2005, :1611-1614

[47] An Energy-Efficient Clustering Algorithm for Wireless Sensor Networks [J].

Matos, Victor de Oliveira ;

Arroyo, Jose Elias C. ;

dos Santos, Andre Gustavo ;

Goncalves, Luciana B. .

INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND NETWORK SECURITY, 2012, 12 (10) :6-15

[48] A new efficient algorithm based on DC programming and DCA for clustering [J].

Le Thi Hoai An ;

M. Tayeb Belghiti ;

Pham Dinh Tao .

Journal of Global Optimization, 2007, 37 :593-608

[49] An efficient hybrid PSO polygamous crossover based clustering algorithm [J].

Manju Sharma ;

Jitender Kumar Chhabra .

Evolutionary Intelligence, 2021, 14 :1213-1231

[50] Energy efficient fuzzy clustering and routing using BAT algorithm [J].

Amruta Lipare ;

Damodar Reddy Edla ;

Ramesh Dharavath .

Wireless Networks, 2021, 27 :2813-2828

← 1 2 3 4 5 →